This paper proposes a new method for automatically counting vehicle turning movements based on video tracking, expanding on previous work on optimization of parameters for road user trajectory extraction and on automated trajectory clustering. The counting method is composed of three main steps: an automated tracker that extracts vehicle trajectories from video data, an automated trajectory clustering algorithm, and an optimization algorithm. The proposed method was applied to obtain turning movement counts in three typical traffic engineering case studies in Canada representing industry-type conditions. These exhibited varying levels of tracking difficulty, ranging from a single-lane off-ramp to a six-movement intersection with a stop and a right-turn channel. Because of a limitation of the data set, giving flows per movement and not per lane, all sites were chosen with a single lane per movement. The 3-h morning peak period was used in the case studies. The results show an average weighted generalization error of 12% for more than 3,700 vehicles automatically analyzed for more than 8 h of video, ranging from 9.5% to 19.5%. The generalization error is on average 8.6% (and as low as 6.0% per movement) for the 3,084 uninterrupted vehicles that are in plain view of the camera. This paper describes in detail the methodology used and discusses the factors that affect counting performance and how to improve counting accuracy in further research.
Optimized Video Tracking for Automated Vehicle Turning Movement Counts
Transportation Research Record
Transportation Research Record: Journal of the Transportation Research Board ; 2645 , 1 ; 104-112
2017-01-01
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Automated Turning Movement Counts for Shared Lanes: Leveraging Vehicle Detection Data
Transportation Research Record | 2016
|Dynamic Origin-Destination Demand Estimation Using Turning Movement Counts
Transportation Research Record | 2008
|Dynamic Origin-Destination Demand Estimation Using Turning Movement Counts
Online Contents | 2008
|Network-level turning movement counts estimation using traffic controller event-based data
Taylor & Francis Verlag | 2023
|